6 months ago
# Sure, let's consider that we have a CSV file with the following columns: Country, Health_Group, and Population. We will use PySpark to load the data, and then we will perform some basic analysis like counting the number of distinct countries, health groups, as well as computing the total and average population. # Please replace 'path_to_your_dataset.csv' with the actual path to your dataset. from pyspark.sql import SparkSession from pyspark.sql.functions import * # Create a SparkSession spark = SparkSession.builder.appName('health_group_analysis').getOrCreate() # Load the dataset df = spark.read.csv('path_to_your_dataset.csv', inferSchema=True, header=True) # Check the schema of the dataset df.printSchema() # Count the number of distinct countries num_countries = df.select('Country').distinct().count() print("Number of distinct countries: ", num_countries) # Count the number of distinct health groups num_health_groups = df.select('Health_Group').distinct().count() print("Number of distinct health groups: ", num_health_groups) # Compute the total population total_population = df.select(sum('Population')).first() print("Total population: ", total_population) # Compute the average population per health group avg_population_per_group = df.groupBy('Health_Group').agg(avg('Population').alias('Average_Population')).sort('Health_Group') avg_population_per_group.show() # Stop the SparkSession spark.stop() # This is a simple analysis. Depending on the specific requirements of your task, you might need more complex queries or data preprocessing. # Running this code requires a Spark environment. If you're running this locally, you'll need to have Apache Spark installed and configured properly. If you're using a cloud-based service like Databricks or AWS EMR, these environments are already set up to run PySpark code.